Next Article in Journal
A Compact Fiber Inclinometer Using a Thin-Core Fiber with Incorporated an Air-Gap Microcavity Fiber Interferometer
Previous Article in Journal
Steady State Response Analysis of a Tubular Piezoelectric Print Head
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(1), 85; doi:10.3390/s16010085

A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting

1
Department of Electronics, University of Alcalá, Alcalá de Henares, Madrid 28805, Spain
2
School of Engineering, University of Portsmouth, Winston Churchill Ave, Portsmouth PO1 3HJ, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 6 November 2015 / Revised: 4 January 2016 / Accepted: 7 January 2016 / Published: 12 January 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [7089 KB, uploaded 14 January 2016]   |  

Abstract

In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make crucial decisions regarding purchasing and generating electric power, load switching, and infrastructure development. In this regard, Short Term Load Forecasting (STLF) allows the electric power load to be predicted over an interval ranging from one hour to one week. However, important issues concerning error detection by employing STLF has not been specifically addressed until now. This paper proposes a novel STLF-based approach to the detection of gain and offset errors introduced by the measurement equipment. The implemented system has been tested against real power load data provided by electricity suppliers. Different gain and offset error levels are successfully detected. View Full-Text
Keywords: Short Term Load Forecasting (STLF); Artificial Neural Network (ANN); measurement error detection; secondary substation (SS) Short Term Load Forecasting (STLF); Artificial Neural Network (ANN); measurement error detection; secondary substation (SS)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Moriano, J.; Rodríguez, F.J.; Martín, P.; Jiménez, J.A.; Vuksanovic, B. A New Approach to Detection of Systematic Errors in Secondary Substation Monitoring Equipment Based on Short Term Load Forecasting. Sensors 2016, 16, 85.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top